A neural network based approach for sentiment classification in the blogosphere

Abstract

Recognizing emotion is extremely important for a text-based communication tool such as a blog. On commercial blogs, the evaluation comments by bloggers of a product can spread at an explosive rate in cyberspace, and negative comments could be very harmful to an enterprise. Lately, researchers have been paying much attention to sentiment classification. The goal is to efficiently identify the emotions of their customers to allow companies to respond in the appropriate manner to what customers have to say. Semantic orientation indexes and machine learning methods are usually employed to achieve this goal. Semantic orientation indexes do not have good performance, but they return results quickly. Machine learning techniques provide better classification accuracy, but require a lot of training time. In order to combine the advantages of these two methods, this study proposed a neural-network based approach. It uses semantic orientation indexes as inputs for the neural networks to determine the sentiments of the bloggers quickly and effectively. Several actual blogs are used to evaluate the effectiveness of our approach. The experimental results indicate that the proposed approach outperforms traditional approaches including other neural networks and several semantic orientation indexes.